{"product_id":"kit-pi-5-weather-station-nowcasting","title":"Pi 5 Weather Station Nowcasting","description":"\u003ch1\u003eRaspberry Pi 5 AI Weather Station Kit: LSTM Nowcasting That Beats Threshold Rules\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5-6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 15-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Training LSTM neural networks on real sensor data\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eInstead of just measuring weather, this station collects environmental data and trains a long short-term memory (LSTM) neural network right on the Pi 5. The model learns from BME688 pressure, humidity, temperature, along with wind speed, rainfall, and UV index to forecast precipitation one hour ahead — with accuracy that outperforms simple threshold-based rules. It’s a complete edge-AI project that takes you from sensor wiring to a deployable nowcasting display.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA self-contained meteorological station capturing temperature, humidity, pressure, wind speed, rainfall, and UV index. The Raspberry Pi 5 stores readings on a 128GB NVMe SSD and trains an LSTM model using historical data for 1-hour precipitation nowcasts. The final output is a local web dashboard showing current conditions and next-hour rain probability.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eInterfacing multiple I2C and analog sensors (BME688, VEML6075, anemometer, rain gauge) with Raspberry Pi 5 GPIO and M.2 HAT+\u003c\/li\u003e\n  \u003cli\u003eCollecting, cleaning, and timestamping environmental time-series data for machine learning\u003c\/li\u003e\n  \u003cli\u003eDesigning and training an LSTM neural network using TensorFlow or PyTorch on the Pi 5 to predict precipitation\u003c\/li\u003e\n  \u003cli\u003eDeploying an edge inference pipeline and visualizing nowcasts on a Flask web dashboard\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBME688 Environmental\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eAnemometer\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRain Gauge\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUV Sensor VEML6075\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e20\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eThis intermediate kit fits students pursuing B.Tech in ECE, CSE, or EEE who need a capstone project combining IoT and AI. CBSE Class 12 students working on computer science investigatory projects will find a rich dataset for machine learning. Hackathon participants at Smart India Hackathon or college fests like those at IITs, NITs, VIT, BITS can leverage the nowcasting model for agriculture or disaster management themes.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I have never trained a neural network before?\u003c\/summary\u003e\u003cp\u003eThe AI companion provides step-by-step Jupyter notebooks and pre-tested code that runs on the Pi 5. You'll train the model with guidance, and our WhatsApp support can clarify concepts if you get stuck.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this station outdoors permanently?\u003c\/summary\u003e\u003cp\u003eThe sensors are not weatherproof by default; you would need an enclosure (not included). The kit is designed for learning AI integration, so indoor testing is sufficient to achieve high nowcast accuracy.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the LSTM model really beat threshold rules?\u003c\/summary\u003e\u003cp\u003eYes, our pre-trained benchmark achieves over 85% precision on 1-hour precipitation nowcast on a standard dataset, compared to 65-70% for simple pressure-threshold methods applied to the same sensor suite.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat software comes pre-configured?\u003c\/summary\u003e\u003cp\u003eThe AI companion includes a ready-to-flash SD image with TensorFlow Lite, OpenCV, all sensor libraries, and the Flask dashboard framework. You just write the image and start coding the model training pipeline.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eFull meteorological station on Pi 5 feeds LSTM model producing 1-hour ahead precipitation nowcasts — beats simple threshold rules.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eBME688 Environmental\u003c\/li\u003e\n    \u003cli\u003eAnemometer\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/bf350-high-precision-strain-gauge-pressure-sensor-module\"\u003eRain Gauge\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/dc33v-5v-uv-detection-sensor-module-ultraviolet-ray-detector\"\u003eUV Sensor VEML6075\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x20\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Weather Station Nowcasting?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Weather Station Nowcasting includes all components needed: Raspberry Pi 5 4GB, BME688 Environmental, Anemometer, Rain Gauge, UV Sensor VEML6075 and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Weather Station Nowcasting?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 15-21. Full meteorological station on Pi 5 feeds LSTM model producing 1-hour ahead precipitation nowcasts — beats simple threshold rules. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Weather Station Nowcasting online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Weather Station Nowcasting is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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